This paper proposes an image processing methodology to address one of the core issues of plant pathology i.e. disease identification and its grading. The proposed system is an efficient module that identifies various diseases of pomegranate plant and also determines the stage in which the disease is. The system employs various image processing and machine learning techniques. At first, the captured images are processed for enhancement. Then image segmentation is carried out to get target regions (disease spots). Later, image features such as shape, color and texture are extracted for the disease spots. These resultant features are then given as input to disease classifier to appropriately identify and grade the diseases. Finally, based on the stage of the disease, the treatment advisory module can be prepared by seeking agricultural experts, there by helping the farmers.

Cashew is a commercial commodity that plays a major role in earning foreign currency among export commodities of India. The sub-sector is getting governmental and non-governmental attention due its significance in commercial activities. The brand patent creation of each cashew varies based on cashew kernels is an issue in current periods. The purpose of this research work is to explore image processing techniques and approaches on Indian cashew variety identification based on their kernels. Colour is an important quality factor for grading, marketing, and end use of Cashew. Our objective is to develop a cost-effective way to identify the cashew kernels. Such a system would not only facilitate cashew grading but also serve as a quality control tool for processing facilities such as grading and sorting in export industries like cashew. This paper presents a methodology for identification and classification of cashew kernels white wholes. The texture features are extracted using gray level co-occurrence matrix method. The multilayer feed forward neural network is developed to classify cashew kernels white wholes. An analysis of the efficiency of methodology is found 90%.

Cashew is a commercial commodity that plays a major role in earning foreign revenue among export commodities in India. The purpose of this research work is to explore image processing techniques and approaches on Indian cashew variety identification based on their kernels. Colour is an important quality factor for grading, marketing, and end users. Our primary objective is to develop a cost-effective intelligent model to identify the cashew kernels. Colour features in the RGB (red-green-blue) colour space are extracted and computed. A feed-forward neural network is trained to classify sample cashew kernels. An intelligent classification system based on computer vision system can be developed for automated grading and sorting to speed up the classification of cashew kernels. This will solve the major problems of many of the cashew export industries also, gives justice to the cashew growing farmers in accurate grading. The classification system is evaluated on cashew kernels of 6 different grades. The result of our study shows that, the system gives about 80% classification rate.

To monitor & control the remotely situated Electro-Mechanical system or system parameters’ we propose novel wireless framework using cell phones‘s SMS facilities. Our system enables the farmer to get the intimation about status both on demand as well as automatically in critical situations viz. water level in the tank or well, voltage and current level, water level reached in the field while pouring water to the crop, pressure, Temperatures etc. in the form of feedback SMS and control the operations of the system accordingly. We also made an attempt to keep the log of the status of various parameters in a PC for future reference. The system has been tested on-field for monitoring and controlling Pump-Set actions and pouring the water to crops based on soil condition. We found that results are very satisfactory and encouraging given the simplicity and cost effectiveness of the framework.

This paper describes a different neural network model for classification and grading of bulk seeds samples using different artificial neural network models. Algorithms are developed to acquire and process color images of bulk seeds samples. Different seeds like Groundnut, Jowar, Wheat, Rice, Metagi, Red gram, Bengal gram, and Lentils etc. are considered for the study. The developed algorithms are used to extract over 11 (9 color, area and equidiameter) features, 18 (color only) features and 20 (18 color and 2 boundary) features. The area and equidiameter features are extracted from the watershed segmentation. Different types of Neural Network based classifier is used to identify the unknown seeds samples. The classification is carried out using different types of features sets, viz., color, area and equidiameter. Classification accuracies of over 85% are obtained for all the seeds types using all the three feature sets. And also different neural network gives different accuracies and time period taken for training all the three feature sets.

Automatic diagnosis of rice plant diseases at an early stage and taking corrective measures in time saves damages of rice crop across the world. The paper aims at developing an appropriate methodology to classify diseases with the help of feature sets obtained by analyzing images of infected rice plants acquired from the field. Since all features are not important in classifying diseases; selection of optimum features is a challenging task to address the problem. The work is performed in three steps. Firstly thirty six features of different category are extracted from the diseased plant images using image processing techniques. Secondly information gain (IG) of each attribute with respect to other attributes is calculated following the concept of information entropy theory. Thirdly using IG, functional dependency of the attributes are evaluated based on which fourteen significant attributes out of thirty six are selected, sufficient to classify the diseases. The proposed method has been applied on four hundred fifty infected rice plant images having three different classes. With the reduced feature set, classification accuracy is calculated using different classifiers demonstrating effectiveness of the proposed model.